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Artificial Intelligence Methods

Abstract

In artificial intelligence, we design software systems that exhibit some form of "intelligent behavior" -- ranging from perceiving the system's environment to making effective decisions in a rational and autonomous manner without human intervention. Traditionally, capabilities associated with intelligence were examined such as language understanding, (logical) reasoning, learning, and decision-making. The ultimate goal is both to incorporate existing knowledge ("world models") into these software agents as well as to equip them with the ability to learn from data they perceive and thus continuously improve. Our group "Artificial Intelligence Methods" particularly focuses on techniques that allow us to address commercial and industrial use cases of artificial intelligence such as recommender systems for mechanical design, learning parameter settings for CFRP (carbon fiber reinforced polymer) applications from simulations and observations, and optimization according to preferences, e.g. in smart energy systems. Achieving these goals requires a blend of machine learning and constraint optimization techniques that we actively research as our two central pillars.
We furthermore investigate how to make foundational techniques in AI accessible by means of developing "engineering practices" and domain-specific languages.

Research Topics

AI Engineering, Machine Learning Engineering

Data Analytics, Structured Probabilistic Models

Deep Learning, Transfer Learning, Simulation2Real, Active Learning

Constraint Programming and Optimization

Soft Constraints and Multiagent Optimization

Applications

We apply these core concepts in various domains, often motivated by industrial partners: